Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach.

Dvey-Aharon Z, Fogelson N, Peled A, Intrator N - PLoS ONE (2015)

Bottom Line:
Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity.This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives.The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

ABSTRACTElectroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

pone.0123033.g005: Accuracy of methodology prediction as a function of time window within each stimulus recording.The most variance (or local peaks of methodology accuracy) is best achieved with a ~300 ms window placed in the recording window post-stimulus.

Mentions:
The results for the optimal [a, b] window within the stimulus recording interval (of each appearance of the ‘P’ stimulus) showed that the most accurate predictions can be achieved at intervals of (a) 200–300 ms post-stimulation and (b) 900–950 ms post-stimulation. These results are presented in Fig 5 below. The interval in (a) can be explained by latency in the analysis process in the frontal area located near the P300 recording area, which is evident from significant visual-related stimuli [26]. The interval in (b) can be explained by the latency of recovery from visual analysis and prediction of the brain activity, which is evident from differences in subjects with mental disorders [26;27;28].

pone.0123033.g005: Accuracy of methodology prediction as a function of time window within each stimulus recording.The most variance (or local peaks of methodology accuracy) is best achieved with a ~300 ms window placed in the recording window post-stimulus.

Mentions:
The results for the optimal [a, b] window within the stimulus recording interval (of each appearance of the ‘P’ stimulus) showed that the most accurate predictions can be achieved at intervals of (a) 200–300 ms post-stimulation and (b) 900–950 ms post-stimulation. These results are presented in Fig 5 below. The interval in (a) can be explained by latency in the analysis process in the frontal area located near the P300 recording area, which is evident from significant visual-related stimuli [26]. The interval in (b) can be explained by the latency of recovery from visual analysis and prediction of the brain activity, which is evident from differences in subjects with mental disorders [26;27;28].

Bottom Line:
Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity.This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives.The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

ABSTRACTElectroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.